regressor_parse_example_spec: Generates Parsing Spec for TensorFlow Example to be Used with...
In tfestimators: Interface to 'TensorFlow' Estimators

Description Usage Arguments Value Raises See Also

If users keep data in tf$Example format, they need to call tf$parse_example with a proper feature spec. There are two main things that this utility helps:

Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf$Example instance. This utility combines these specs.
It is difficult to map expected label by a regressor such as dnn_regressor to corresponding tf$parse_example spec. This utility encodes it by getting related information from users (key, dtype).

regressor_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$float32,
  label_default = NULL,
  label_dimension = 1L,
  weight_column = NULL
)

`feature_columns`	An iterable containing all feature columns. All items should be instances of classes derived from `_FeatureColumn`.
`label_key`	A string identifying the label. It means `tf$Example` stores labels with this key.
`label_dtype`	A `tf$dtype` identifies the type of labels. By default it is `tf$float32`.
`label_default`	used as label if label_key does not exist in given `tf$Example`. By default default_value is none, which means `tf$parse_example` will error out if there is any missing label.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `[batch_size, label_dimension]`).
`weight_column`	A string or a `_NumericColumn` created by `column_numeric` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features`. If it is a `_NumericColumn`, raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

ValueError: If label is used in feature_columns.
ValueError: If weight_column is used in feature_columns.
ValueError: If any of the given feature_columns is not a _FeatureColumn instance.
ValueError: If weight_column is not a _NumericColumn instance.
ValueError: if label_key is NULL.